Recent years have seen a shift in music listening habits from physical media such as Compact Discs (CDs) and cassettes to digital media stored on the user's playback device such as Moving Pictures Experts Group Layer 3 (MP3) files. This transition has made music much more accessible to listeners worldwide. However, the increased accessibility of music has only heightened a long-standing problem for the music industry, which is namely the issue of linking audiophiles with new music that matches their listening preferences.
Many companies, technologies, and approaches have emerged to address this issue of music recommendation. Some companies have taken an analytical approach. They review various attributes of a song, such as melody, harmony, lyrics, orchestration, vocal character, and the like, and assign a rating to each attribute. The ratings for each attribute are then assembled to create a holistic classification for the song that is then used by a recommendation engine. The recommendation engine typically requires that the user first identify a song that he or she likes. The recommendation engine then suggests other songs with similar attributions. Companies using this type of approach include Pandora (http://www.pandora.com), SoundFlavor (http://www.soundflavor.com), MusicIP (http://www.musicip.com), and MongoMusic (purchased by Microsoft in 2000).
Other companies take a communal approach. They make recommendations based on the collective wisdom of a group of users with similar musical tastes. These solutions first profile the listening habits of a particular user and then search similar profiles of other users to determine recommendations. Profiles are generally created in a variety of ways such as looking at a user's complete collection, the playcounts of their songs, their favorite playlists, and the like. Companies using this technology include Last.fm (http://www.last.fm), Music Strands (http://www.musicstrands.com), WebJay (http://www.webjay.org), Mercora (http://www.mercora.com), betterPropaganda (http://www.betterpropaganda.com), Loomia (http://www.loomia.com), eMusic (http://www.emusic.com), musicmatch (http://www.mmguide.musicmatch.com), genielab (http://genielab.com/), upto11 (http://www.upto11.net/), Napster (http://www.napster.com), and iTunes (http://www.itunes.com) with its celebrity playlists.
The problem with the prior art is that it fails to incorporate a pop culture historical context to music recommendation. For many users, musical preferences are heavily influenced by the type and nature of music that was popular at the time they became interested in music, as witnessed by the popularity of oldies radio stations (i.e., stations playing music from the 1950s) and classic rock stations (i.e., stations playing music from the 1970s and 1980s). Further, their musical tastes are also influenced by the type and nature of other media (such as movies and television) that were popular at particular points in time. For instance, movies such as Saturday Night Fever, Grease, Chariots of Fire, Top Gun, Footloose, Flashdance, The Breakfast Club, and Pretty Woman and television shows such as MASH, Cheers, Happy Days, Fame, and Moonlighting all had soundtracks that enjoyed great popularity in their respective time periods.
For example, a user may like a particular artist from the early 1980s, such as Michael Jackson, but they might also enjoy songs from the television show Fame and the film Top Gun. However, recommendation systems based on music similarity would fail to make these suggestions to the user. As for communal based recommendation systems, they may perform marginally better in this case. More specifically, since communal based recommendation systems rely primarily on musical relationships set forth by particular users, they will inherently reflect some degree of pop culture association in their music recommendations. In other words, some people will remember artists, songs, and movies that were popular at the same time as another given artist or song or movie, and their collections and profile will reflect these associations. However, memory can be unreliable, and it is dubious that one user or collection of users can make all of the associations.
Therefore, there is a need for a media recommendation system that provides recommendations based on a historical context.